Poster No:
1555
Submission Type:
Abstract Submission
Authors:
WOO YOUNG KANG1, Yen-Cheng Liu1, Chun-Wei Hsu2, Lin-han Huang2, Yi-Xin Fang2, Joshua Goh2
Institutions:
1Graduate Institute of Brain and Mind Science, National Taiwan University, Taipei, Taiwan, 2Graduate Institute of Brain and Mind Sciences, National Taiwan University, Taipei, Taiwan
First Author:
Wooyoung Kang
Graduate Institute of Brain and Mind Science, National Taiwan University
Taipei, Taiwan
Co-Author(s):
Yen-Cheng Liu
Graduate Institute of Brain and Mind Science, National Taiwan University
Taipei, Taiwan
Chun-Wei Hsu
Graduate Institute of Brain and Mind Sciences, National Taiwan University
Taipei, Taiwan
Lin-han Huang
Graduate Institute of Brain and Mind Sciences, National Taiwan University
Taipei, Taiwan
Yi-Xin Fang
Graduate Institute of Brain and Mind Sciences, National Taiwan University
Taipei, Taiwan
Joshua Goh
Graduate Institute of Brain and Mind Sciences, National Taiwan University
Taipei, Taiwan
Introduction:
Functional communication between neural networks across voxels depends on the distance between them on the grey matter (GM) manifold as well as white matter (WM) pathways. Precise understanding of how functional communication is modulated by structural constraints requires a voxel-level approach to quantify the local and long-range relationships. Traditional group- and brain-regional-based methods often rely on normalisation, limiting their subject-specific precision, and reduce data through averaged signals, limiting access to voxel-level patterns. Here, we consider individual, voxel-level data when integrating functional connectivity (FC), GM inter-voxel geodesic distances, and WM diffusion path probabilities. We evaluate a unified approach to bridge structural and functional measures, offering a quantitative perspective on functional brain organization based on real physical constraints.
Methods:
Diffusion-weighted (DWI) and resting functional magnetic resonance (fMRI) imaging data were preprocessed without normalisation to preserve subject-specific voxel resolution. Grey matter inter-voxel geodesic distances were calculated using Dijkstra's algorithm, providing shortest paths constrained by cortical topology to ensure base biologically plausible connectivity estimates. GM-WM boundary voxels, identified via tissue segmentation, served as seeds for probabilistic tractography using FSL's probtrackx tool, enabling the estimation of inter-voxel white matter pathway connectivity. FC was derived from voxel-wise time-series correlations within grey matter tissue, indexing temporal response similarity between voxels. These three matrices-FC, geodesic distances, and diffusion probabilities-were integrated into a unified voxel-level framework to explore spatial relationships and connectivity patterns.
Results:
The functional connectivity matrix revealed localised clusters of strong temporal correlations between spatially close grey matter voxels, as expected, as well as long-range functional correlations. Moreover, FCs decreased with greater geodesic distances on the GM manifold. The WM diffusion matrix showed high probabilities concentrated between spatially close voxels, with lower probabilities observed for longer pathways. Visualisation of these relationships within a unified 3D voxel-based framework integrated these components. Specifically, there was a clear alignment between structural proximity and connectivity strength. Voxels with shorter GM geodesic distances exhibited higher FC and stronger WM path probabilities, while longer distances corresponded to weaker functional and structural connections. Critically, there were also strong FCs at short-range GM distances with low WM path probabilities, long-range GM distances coupled with high WM path probabilities, but no notable FCs at long-range GM distances with low WM path probabilities.
Conclusions:
This study introduces a voxel-wise, individualized data analysis framework that integrates FC, GM geodesic distances, and WM diffusion path probabilities. Our approach quantifies local and long-range relationships, bridging structural and functional domains at voxel-level rather than regional resolution. This methodology provides a quantitative tool for understanding functional brain organisation, enabling a unified analysis of structural and functional images across the whole brain system at the most precise level afforded by image data. These findings suggest that voxel-based frameworks can provide deeper insights into local and global brain connectivity patterns, offering advantages over conventional approaches. Future work will focus on extending this framework to evaluate characterizations of individual differences.
Modeling and Analysis Methods:
Methods Development 1
Task-Independent and Resting-State Analysis 2
Keywords:
MRI
STRUCTURAL MRI
Structures
White Matter
Other - intervoxel relationship
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Functional MRI
Structural MRI
Diffusion MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
SPM
FSL
Provide references using APA citation style.
Cammoun, L., Gigandet, X., Meskaldji, D., Thiran, J. P., Sporns, O., Do, K. Q., Maeder, P., Meuli, R., & Hagmann, P. (2012). Mapping the human connectome at multiple scales with diffusion spectrum MRI. Journal of Neuroscience Methods, 203(2), 386–397. https://doi.org/10.1016/j.jneumeth.2011.09.031
Benkarim, O., Paquola, C., Park, B.-Y., Royer, J., Rodríguez-Cruces, R., Vos de Wael, R., Misic, B., Piella, G., & Bernhardt, B. C. (2022). A Riemannian approach to predicting brain function from the structural connectome. NeuroImage, 257, 119299. https://doi.org/10.1016/j.neuroimage.2022.119299
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